These studies are directed toward evaluating the prognostic power of the electrocardiogram, when analyzed by advanced computer methodology, and the predictive accuracy of diagnostic criteria, when implemented in ECG computer programs. Appropriate use of digital signal processing in electrocardiography requires application of statistically-based techniques of information theory and mathematically-based engineering methods, as well as knowledge of its clinical relevance. Additional studies are directed toward the analysis of heart rate, blood pressure and respiratory signals that affect syncopal patients during table-tilt testing, using autoregressive models and the corresponding power spectra. Syncope can be disabling for patients and, at times, life threatening. An understanding of the autonomic nervous system mechanisms responsible for syncope may indicate appropriate therapy. These studies have been re-directed toward the analysis of ambulatory electrocardiography (AECGs). Despite extensive literature showing that information extracted by computer analysis of AECGs can be related to cardiac risk factors, there are no standard methods for the routine analysis of AECGs in this rapidly evolving field. The objective of this research is to carry forward previous work in biosignal analysis and to adapt methodologies, with the goal of implementing as much automation as possible to enable and expedite the interpretation of the huge streams of AECG data.